Antidepressant Medications: Biology, Exposure
& Response (AMBER)


Study Overview

The AMBER study (Antidepressant Medications: Biology, Exposure & Response) is a Wellcome-funded Mental Health Award that aims to identify the causal determinants of antidepressant response (2023-2028).

Our research is carried out in collaboration with people who have lived experience of depression and antidepressant treatment. This partnership aims to increase trust, ensure the relevance and significance of our research, and improve the dissemination and impact of the research findings.

King’s investigators, Cathryn Lewis and Oliver Pain, work in collaboration with researchers at the University of Edinburgh (Andrew McIntosh, Heather Whalley, Sue Fletcher-Watson) and the University of Queensland (Sonia Shah, Quan Nguyen, Naomi Wray) to achieve this ambitious project.


Our work so far


Publications

To date, we have published several papers/preprints:

  • A meta-analysis of clinical trials with genetic data (in collaboration with the Psychiatric Genomics Consortium), showing antidepressant response to be significantly heritable (Pain et al., 2022)
  • An imputation algorithm and meta-analysis to study the role of CYP2C19 and CYP2D6 enzyme metabolic activities on antidepressant response (Li et al., 2024)
  • A descriptive study on the social, demographic and genetic characteristics of self-reported antidepressant response in UK Biobank participants (Kamp et al., 2025)
  • A phenotyping algorithm of SSRI non-response by capturing switching patterns, using prescribing records in UK Biobank and Generation Scotland (Lo et al., 2025)


Learn More


AMBER Blog


Learn More

Contacts

For details on AMBER project, please visit our King’s website here.

We welcome new collaborators with relevant data sources on any aspect of antidepressants: email .


Any questions?

Please post questions as an issue on the T-Rx GitHub repo here.

The T-Rx package is currently under beta testing. Most functions should have adequate documentation on possible errors.

Please kindly reach out to Chris Lo () for feedback on documentation.